Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89691
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dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorBenjaafar, Sen_US
dc.creatorWu, Sen_US
dc.creatorLiu, Hen_US
dc.creatorGunnarsson, EBen_US
dc.date.accessioned2021-04-28T02:29:18Z-
dc.date.available2021-04-28T02:29:18Z-
dc.identifier.issn0025-1909en_US
dc.identifier.urihttp://hdl.handle.net/10397/89691-
dc.language.isoenen_US
dc.publisherInstitute for Operations Research and the Management Sciencesen_US
dc.rights© 2021 INFORMSen_US
dc.rightsThis is the accepted manuscript of the following article: Saif Benjaafar, Shining Wu, Hanlin Liu, Einar Bjarki Gunnarsson (2021) Dimensioning On-Demand Vehicle Sharing Systems. Management Science 68(2):1218-1232, which has been published in final form at https://doi.org/10.1287/mnsc.2021.3957.en_US
dc.subjectOn-demand vehicle sharing systemsen_US
dc.subjectClosed queueing networksen_US
dc.subjectCapacity optimizationen_US
dc.subjectBounds and approximationsen_US
dc.titleDimensioning on-demand vehicle sharing systemsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1218en_US
dc.identifier.epage1232en_US
dc.identifier.volume68en_US
dc.identifier.issue2en_US
dc.identifier.doi10.1287/mnsc.2021.3957en_US
dcterms.abstractWe consider the problem of optimal fleet sizing in a vehicle sharing system. Vehicles are available for short-term rental and are accessible from multiple locations. A vehicle rented at one location can be returned to any other location. The size of the fleet must account not only for the nominal load and for the randomness in demand and rental duration but also for the randomness in the number of vehicles that are available at each location because of vehicle roaming (vehicles not returning to the same location from which they were picked up). We model the dynamics of the system using a closed queueing network and obtain explicit and closed form lower and upper bounds on the optimal number of vehicles (the minimum number of vehicles needed to meet a target service level). Specifically, we show that starting with any pair of lower and upper bounds, we can always obtain another pair of lower and upper bounds with gaps between the lower and upper bounds that are independent of demand and bounded by a function that depends only on the prescribed service level. We show that the generated bounds are asymptotically exact under several regimes. We use features of the bounds to construct a simple and closed form approximation that we show to be always within the generated lower and upper bounds and is exact under the asymptotic regimes considered. Extensive numerical experiments show that the approximate and exact values are nearly indistinguishable for a wide range of parameter values. The approximation is highly interpretable with buffer capacity expressed in terms of three explicit terms that can be interpreted as follows: (1) standard buffer capacity that is protection against randomness in demand and rental times, (2) buffer capacity that is protection against vehicle roaming, and (3) a correction term. Our analysis reveals important differences between the optimal sizing of standard queueing systems (where servers always return to the same queue upon service completion) and that of systems where servers, upon service completion, randomly join any one of the queues in the system. We show that the additional capacity needed to buffer against vehicle roaming can be substantial even in systems with vanishingly small demand.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationManagement science, Feb. 2022, v. 68, no. 2, p. 1218-1232en_US
dcterms.isPartOfManagement scienceen_US
dcterms.issued2022-02-
dc.identifier.eissn1526-5501en_US
dc.description.validate202104 bcvcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0830-n01-
dc.identifier.SubFormID1971-
dc.description.fundingSourceRGCen_US
dc.description.fundingText25504517, 15507919en_US
dc.description.pubStatusPublisheden_US
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